Good customer experiences lead to increased lifetime value and loyalty. With the acceleration of digital platforms and e-commerce for major retail brands due to the pandemic, retailers are racing to capture brand loyalty through consistent and engaging virtual shopping experiences. Recommender models help ensure we’re offering relevant products and promotions through mining purchase history across customers and their peers.
E–commerce sales for grocery brands has jumped 6% in 2020. This represents a drastic $60B increase for online grocery purchases in less than a year.
Brands are scrambling to improve customer experiences. Various hybrid shopping models including curbside pickup, in-store pickup, and last-mile or 3rd party delivery services are making it increasingly challenging for consumer experiences to be consistent across channels.
A brand’s ability to create consistently positive experiences across their in-person, online, and hybrid channels will likely be the difference between thriving versus merely surviving during the pandemic. An additional benefit of creating consistent customer experiences across your digital channels is that it will ultimately lead to increased predictability in all functions, including efficient operations, inventory planning, and workforce optimization.
We’ve all seen how powerful recommender models can be when shopping on well–known e–commerce platforms, but small to mid-size grocers have a huge opportunity to fine–tune their customers interests and experiences using this proven machine learning practice.
We recently presented an in-depth analysis of how grocers can use recommender models, leveraging powerful Azure data services like Azure Databricks, in our white paper “The Peanut Butter Problem.”
If you’re interested in learning more we’d welcome a discussion with you and your team. Please contact us today!
